Databricks Certified-Data-Engineer-Professional : Databricks Certified Data Engineer Professional

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Databricks Certified-Data-Engineer-Professional Exam Syllabus Topics:

Databricks Certified Data Engineer Professional Sample Questions:

1. When a new Databricks project starts, the central IP team provisions the required infrastructure using Terraform and a Service Principal. This includes creating a Databricks workspace, a Unity Catalog linked to an External Location, and a Databricks group containing all project team members. Project teams must store all assets - e.g., tables and volumes, as Managed assets in Unity Catalog. This model hides infrastructure complexity while giving teams autonomy within their catalog. They can create and manage schemas, tables, volumes, and related objects but cannot rename, delete, or change catalog permissions, those remain under IT's control. Which rights should the project group be granted to enable this model?

A) The group needs to have ALL PRIVILEGES and the MANAGE on the catalog.
B) The group needs to have USE CATALOG and USE SCHEMA on the catalog.
C) The group needs to have ALL PRIVILEGES on the catalog.
D) The group should be made OWNER of the catalog.

2. A Data engineer wants to run unit's tests using common Python testing frameworks on python functions defined across several Databricks notebooks currently used in production. How can the data engineer run unit tests against function that work with data in production?

A) Define units test and functions within the same notebook
B) Run unit tests against non-production data that closely mirrors production
C) Define and import unit test functions from a separate Databricks notebook
D) Define and unit test functions using Files in Repos

3. A data engineer is reviewing the PySpark code to copy a part of the production dataset to the sandbox environment, and needs to be sure that no PII(Personally Identifiable Information) data is being copied. After checking the sales table, the data engineer notices that it has user emails as the only PII data included as well as being the only column to identify the user.
from pyspark.sql import functions as F

Which anonymised code should be used to achieve the required outcome?

A) df.withColumn ("user_email", F.sha2 ("user_email"))
B) df.withColumn ("hashed_email", sha2 ("user_email"))
C) df.withColumn ("user_email", F.regexp_replace ("user_eamail", "@*", "@anonymized.com"))
D) df.withColumn ("user_emai", F.expr("uuid()"))

4. A data engineer needs to implement column masking for a sensitive column in a Unity Catalog- managed table. The masking logic must dynamically check if users belong to specific groups defined in a separate table (group_access) that maps groups to allowed departments. Which approach should the engineer use to efficiently enforce this requirement?

A) Create a view without selecting the sensitive column.
B) Create a UDF that hardcodes allowed groups and apply it as a column mask.
C) Use a row filter to restrict access based on the user's group.
D) Apply a column mask that references the group_access mapping table in its UDF.

5. A data engineer is brining an existing production Databricks job under asset bundle management and wants to ensure that:
- The job's current configuration is captured as YAML, and all
referenced files are included in their bundle project.
- Future changes to the bundle's YAML will update the existing job in-
place (not create a new job)
How should the data engineer successfully move the production job under asset bundle management?

A) Run databricks bundle generate job --existing-job-id to generate the YAML and download referenced files. Then, run Databricks bundle deployment, bind to link the bundle's job resource to the existing job in Databricks.
B) Export the job definition as JSON, convert it to YAML, and place it in your bundle. Then, run Databricks bundle deploy to update the existing job.
C) Manually create the YAML configuration for the job in your bundle project, ensuring all settings match the existing job. Then, run Databricks bundle deploy the bundle, which will update the existing job in your workspace.
D) Run Databricks bundle generate job --existing-job-id to generate the YAML and download referenced files. Then, run Databricks bundle deploy to deploy the bundle, which will always update the existing job automatically.

Solutions: